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Informatica World 2006 - MDM Data Quality
 

Informatica World 2006 - MDM Data Quality

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Database Architechs is a database-focused consulting company for 17 years bringing you the most skilled and experienced data and database experts with a wide variety of service offering covering all ...

Database Architechs is a database-focused consulting company for 17 years bringing you the most skilled and experienced data and database experts with a wide variety of service offering covering all database and data related aspects.

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    Informatica World 2006 - MDM Data Quality Informatica World 2006 - MDM Data Quality Presentation Transcript

    • Creating Data Quality Rigor for Your Core Data Categories Paul Bertucci Enterprise Data Architect
    • Agenda • Initiative-based data strategy • What must be done to execute on this strategy • A data architecture to support you • A data category example (Customer data) • Making the strategy a way of life • Q&A 2
    • Initiative-based data strategy 3
    • Initiative-based Data Strategy Strategy Initiatives • Identifies key benefits • Short duration • Seeks out alignment • Specific ROI • Sets direction and priorities. • Incremental Initiative-based Activities Foundational Implementations creating incremental value activities ... • Mandated • Enterprise-wide Architecture • Ensure business Data alignment Strategy • Focused on data Data Quality management and infrastructure. Governance Foundational Activities 6
    • What must be done to execute on the strategy 7
    • What Must Be Done • Focusing on enabling key business initiatives with the data they need! • Introduce data governance for all critical data (orders, product, employee…) • Enhance/increase data quality across the board (rules, gates, process, tools) • Move to a data services approach (highly sharing/leveraging data) • Provide data SLA’s for data availability, integrity/quality, 8
    • What Must Be Done (cont’d) • Eliminate data redundancies (across the board) - decrease P2P’s • Put into play data integration capabilities to enable M & A, accelerate current systems consolidations (merger), and support other group or divisional data acquisition in line with the business speed • Move to data hub concepts for key enterprise data (Customer, Product,..) and other enabling tools (e.g. HM) to elevate your ability to do Master Data Management 9
    • What Must Be Done (cont’d) • Roll-out a data certification process for all data sources across the enterprise • Create and maintain an enterprise reference view of data (reference layer) and leverage industry based models where ever possible (e.g. Party– based models) • Protect/secure the data (security/privacy guidelines, roles, DR, backup, archiving) 10
    • A data architecture to support you 11
    • Strategic Data Architecture Applications/Components ODS Transactional Systems Data Next xODS REPL Next Extranet System Analytics ERP – Next Acquisition xODS RC Extranet Transactional Next REPL Other Transactional DW Customer Next ERP 1 Dimensions ODS ODS Hier Mgmt ERP 2 ETL Master Data Management CDH ODS Data Enrich ERP 1 Data Meta ODS WHSE ERP 2 Abstracted Services Data Next Data Hub ODS Customer Next Common META SFA Data Hub Service DATA Product/Pricing MMD DW MetaData ODS Services Data Hub Services Services Services Data Hub License Key Services Generation Application Integration Services EAI Backbone Web Services * Business Objects * Portal * Other 12
    • A data category example (Customer data) 13
    • Customer Data Strategy “Defining and setting how you will effectively identify, manage and leverage customers and their core attributes across all segments to best serve the business now and into the future.” BUSINESS CUSTOMER CONSUMER SMALL BUSINESS MID-MARKET ENTERPRISE PARTNER CUSTOMER CUSTOMER CUSTOMER CUSTOMER Software Other Segments Government Education Developers ... TBD 14
    • Some of the Problems (Symptoms) • Can’t recognize your customers completely (or not at all sometimes) • Burn lots of energy/$ with duplicate data entry, consolidations, roll-ups & reporting . . . . and still don’t have good information. • Must apply data hygiene, corrections, reconciliation in multiple places (not scalable, not consistently applied, out of control). Data Orders Warehousing Reporting Transaction Processing Finance Customers Finance EDI Reports Partners Direct CRM ERP Edu SFA Partner Svcs Leads CRM Sales Reports Edu = “data hygiene/correction/reconciliation” ERP 15
    • Some of the Problems (Symptoms) (cont’d) • Don’t share a common view of your customers/partners, and can’t provide one to THEM, even when they ask. • Don’t know what customers own (licenses, maintenance, subscriptions), and can’t assess compliance, coverage or cross-sell opportunities. • Struggle to append external information (enrichment) • Have difficulty measuring sales effectiveness. Data Orders Warehousing Reporting Transaction Processing Finance Customers Finance EDI Reports Partners Direct CRM ERP Edu SFA Partner Svcs Leads CRM Sales Reports Edu = “data hygiene/correction/reconciliation” ERP 16
    • How Do You Identify a Customer? CONSUMER CUSTOMER CONSUMER Account_ID, Email Address, Per_ID, Order_ID, CUSTOMER Login ID, Name [+], Renewal_ID, others? BUSINESS CUSTOMER SMALL BUSINESS MID-MARKET ENTERPRISE CUSTOMER CUSTOMER CUSTOMER Contact ID, Party ID, Portal_ID, Company ID, Customer Nbr, PROSPECT DUNS Nbr, Name, Canonical ID, CONTACT LEAD OPPORTUNITY CUSTOMER Support ID, others? PARTNER PARTNER Partner Nbr, Party ID, others? PARTNER CUSTOMER CHANNELS 17
    • Customer Data Dilemma ERP ERP CRM SFA D&B (M&A) (enrichment) Customer Customer Customer Customer Customer A B B B B Customer Customer Customer Customer Customer B D (B) F H H Customer Customer Customer Customer Customer C E G I X ERP ID Party ID CRM ID Contact ID DUNS # No strategy or consistency within a silo, or across silo’s 18
    • A Customer Data Strategy Should Provide: • Consistent customer identification & recognition • A single, consistent technique for recognizing and enumerating customers (identification abstraction), sophisticated matching capabilities (Fuzzy, AKA’s, so on), de-duping, merging, etc… • Model-driven (party-based models, so on) • Customer relationships & hierarchies • Enables complex associations to our other customer data (services, sales, opportunities, support, marketing, renewals, so on) to provide the needed 360-degree views of customer data • Support multiple customer hierarchy views for different lines of business (Fin, Sales, …) 19
    • A Customer Data Strategy Should Provide: (cont’d) • Customer data enrichment (internal/external) • Enables any critical data expansion or data enrichment from both internal systems (i.e. “customer segment classification”) and external sources (D&B, HH, Axxiom, so on) • Customer data stewardship (reconcile/resolve/publish/ownership) • Group with sole customer data management responsibility with appropriate counterparts out in each line of business (extended/federated model) 20
    • A Customer Data Strategy Should Provide: (cont’d) • Customer data quality/consistency/full life cycle management • Single “stable” approach to applying data standards, data cleansing, data quality metrics measurement, auditing, and exceptions processing across the full life cycle for this core customer data 21
    • Aligned With the Business • Supporting prospecting (lead, opportunity) • Supporting order quoting • Supporting order capture (all channels) • Supporting marketing campaigns • Supporting customer service/support • Supporting cross-sell/up-sell opportunities • Supporting customer loyalty programs • Supporting licensing/entitlements • Supporting renewal • Resolve financial reporting inconsistencies • Compliance evaluation/customer G2 • Enabling 360-degree views that span different systems Marketing Sales Fulfillment Service Sales Contact/ Market Response Lead Opportunity Quote Order Fulfill Service Support Renewal 22
    • Customer Data Across the Enterprise Customer Data Management Customer Order (ERP) Customer Sales (SFA) Customer Customer Support • Customer ID • Customer ID • Customer ID • Customer ID • ERP Customer Number • SFA Customer Number • CS Customer Number • Customer Type • ERP Cust Master Details • SFA Cust Mast Detail • Titan Cust Master Details • Initial Source • Sales Classifications • Support Classifications • Primary Contact Details • Support Entitlements • Hierarchy Info (D&B) • Classification Details Customer Intelligence Partner Master Marketing Customer • Customer ID • Customer ID • Customer ID • DUNS Info • Partner ID • Marketing Cust Details • Customer Profile Data • Customer Profile Data (Harte Hanke, D&B 1784, (Harte Hanke, D&B, SFA SFA Intelligence) Intelligence) ROLE LOCATION MDM PURPOSE ROLE LOCATION GROUP ADDRESS CONTRACT LOCATION ADDRESS GROUP CONTACT ROLE CONTACT METHOD METHOD GROUP ROLE IDENTIFIER IDENTIFIER RELATION- NAME SHIP EQUIVA- LENCY SALES ENTITY MACRO PERSON ROLE ORGANIZATION GROUP 23
    • Making the strategy a way of life 24
    • Model-driven Customer Data Management IT/Data Architecture ROLE LOCATION PURPOSE ROLE LOCATION GROUP ADDRESS CONTRACT LOCATION ADDRESS CONTACT ROLE GROUP CONTACT METHOD METHOD GROUP ROLE IDENTIFIER IDENTIFIER RELATION- NAME SHIP EQUIVA- LENCY SALES MACRO ENTITY PERSON ROLE ORGANIZATI ON GROUP Customer Model Standardization Systems/Applications Business (CDM) Marketing Sales Fulfillment Service Sales Contact/ Market Response Lead Opportunity Quote Order Fulfill Service Support Renewal 25
    • How the Strategy Becomes Reality Common Project-level Party-based Customer Models Model ERP ROLE LOCATION PURPOSE ROLE LOCATION GROUP ADDRESS CONTRACT ROLE LOCATION ADDRESS GROUP CONTACT METHOD GROUP CONTACT METHOD Drives ROLE IDENTIFIER IDENTIFIER RELATION- NAME SHIP EQUIVA- LENCY MACRO ROLE SALES ENTITY PERSON ORGANIZATI ON Consistent with GROUP CRM 26
    • Movement to Data Hubs (MDM) First up, a customer data hub Customer Data Interactions Partner Other Integration Services ERP CRM Partner Other Finance Reporting Customer Data Hub Sales Reporting ERP CRM Partner Customer DB Customer DB Customer DB Data Quality ERP CRM 27
    • Data Hub Criteria (To Qualify) Data that is created/updated/deleted in more than one place Data that has a need to be highly consistent (across many sources) Data that requires many views (e.g. 360 view of Customer) Data/Attributes that must live on their own Data that must be correlated with other sources (e.g. D&B) Data that must be highly available Data that must be readily accessible (high performance) Data that must have the high integrity Data that requires a formal change management process Data that requires abstracted (enterprise) rules enforcement such as Global Customer ID's (canonical ID's). 28
    • SVC . . . . M&A 360 ° Customer Transaction Views SFA Customer Customer Data Hub ID Mgmt Customer Customer CRM Data Hub Service Data Data Business Recognition Enrichment Rules Customer Data Data Data Loyalty Standardization Cleansing Purge/Arch ERP Data Customer Data Auditing Data Model Versioning Etc. ODS Analytics Views Integration services BPEL Real Time WS EAI ETL/EII DW Analytics Historical Business Objects/Portal/Applications Analytics 29 DM
    • 360-degree View of Customer • ERP • Customer support • Services • Partner systems • Consulting services • Sales force automation • CRM • Contacts/leads • Data enrichment (D&B, Harte Hanks, …) 30
    • Customer Abstraction Sales Entity “100022” [“General Electric”] 1 N Role/Relationship “ERP System” “CRM System” N “DUNS System” N Specific Reference “118902” “342990667” “29903689” • Provides the insulation from moving parts (“n” customer sources) • Provides a consistent representation to apply data rules, standards, and guidelines • Provides a strategic basis for tools or systems (Data Hubs, ERP, CRM, Reporting…) • Highly flexible for M & A and data leveraging (exposing customer views) 31
    • Summary • Make sure you are aligned with what the business needs • Go after one core data category first ! • Leverage industry tools/models if possible • Establish a data quality paradigm/group • Be initiative based with incremental value 32
    • Abstract Trying to solve the data quality issues across multiple divisions, acquisitions, and user realms often leads to failure. Fundamental process and tooling can greatly reduce these failures across the board if they are focused on the primary (core) data categories of your business. Raising the quality of this core data has a ripple affect throughout the organization. In this session, you’ll learn how to identify what the data quality problems are, what needs to be fixed, what type of organization structure is needed, what type of data guidelines and data strategy must be present, and which tools of the trade you need to be successful in delivering all the benefits of high- quality data to your organization. 33